Artificial Intelligence Post Number 23

In earlier updates I emphasized how I believe that true artificial intelligence will come from an accumulation of various targeted self-learning algorithms that go after specific areas. I said that that such algorithms may already exist for the stock market, but these algorithms are not viewable because people or companies are using them to make money in the market, and they are aware that the disclosure of these algorithms may make them useless or cause laws to be passed against their usage.

In my last blog I went into some detail on how Tesla’s AutoPilot is a self-learning system, and how similar systems are likely to grow into true autonomous cars.

Another area I have noted as being ripe for AI is medical. In a 10/15/2015 article in ScienceDaily (http://www.sciencedaily.com/releases/2015/10/151007084259.htm), they discuss “a new diagnostic technology based on advanced self-learning computer algorithms which — on the basis of a biopsy from a metastasis — can with 85 per cent certainty identify the source of the disease and thus target treatment and, ultimately, improve the prognosis for the patient… The newly developed method, which researchers are calling TumorTracer, are based on analyses of DNA mutations in cancer tissue samples from patients with metastasized cancer, i.e. cancer which has spread.”

Even more exciting is that “Researchers expect that, in the long term, the method can also be used to identify the source of free cancer cells from a blood sample, and thus also as an effective and easy way of monitoring people who are at risk of developing cancer.”

2 Responses to “Artificial Intelligence Post Number 23”

I suggest you read a blog entry today by Ben Hunt called “Epsilon Theory”: Two Discoveries. Focus on the second discovery of an algorithm discovery by a university of Chicsgo Prof that may be paradigm shifting (to use a cliche). It’s a little beyond me but I think it may be important. Certainly Ben Hunt thinks it’s important. Let me know what you think. I can’t give you a link because I am writing this on my cell phone. However, you should be able to find it. A lot of financial guys like me read his stuff.

Bob Kaufman suggested I look at Ben Hunt’s second discovery on his blog. When I looked at the source he listed it seems that Ben didn’t read it carefully, because the source stated “…even though networks and graphs are everywhere you look nowadays, the new algorithm isn’t likely to have broad practical applications…”

The algorithm, if proven to be true, enables testing whether two networks of data are identical even though their formats are different. The reason this is not of practical importance is usually we are not testing for total identity but partial identity. For example, if we had the world’s data available and we wanted to see if someone was working with a known terrorist, we would not require that every contact the suspect had was identical to the known terrorist’s contacts. Even a partial match would be sufficient to merit further investigation.

Most breakthroughs on AI involve using algorithms that look for approximations, not mathematical exactness. That is what the human mind does, and it is why we can see correlations that sometimes are cause-and-effect even though pure math did not trigger the observation.